I have used Databricks labs - DQX for a failrly complex data quality implementation and it handlled the mix of built in validations and custom business rules well. One thing we learned is that performance depended much more on the underlying data layout (partitioning, file sizes etc.) than on DQX iteself - if data was already optimized the framework scaled nicely.

One capability that stood out for us was AI assisted rule generation. We started with no predefined validation rules and wanted business users to contribute without relying on data engineers to code manually for every check. Business users provided validation checks in natural language, DQX generated rules, and data engineers reviewed and approved them before deployment. The workflow helped to capture business intent while maintaining technical governance. 

If anyone interested, I put together a small proof-of-concept notebook demonstrating AI assisted workflow:

https://github.com/gbhogle1789/Data-Quality-Frameworks/tree/main/DQX%20DEMO